An Approach Based on Tunicate Swarm Algorithm to Solve Partitional Clustering Problem

Öz

The tunicate swarm algorithm (TSA) is a newly proposed population-based swarm optimizer for solving global optimization problems. TSA uses best solution in the population in order improve the intensification and diversification of the tunicates. Thus, the possibility of finding a better position for search agents has increased. The aim of the clustering algorithms is to distributed the data instances into some groups according to similar and dissimilar features of instances. Therefore, with a proper clustering algorithm the dataset will be separated to some groups with minimum similarities. In this work, firstly, an approach based on TSA algorithm has proposed for solving partitional clustering problem. Then, the TSA algorithm is implemented on ten different clustering problems taken from UCI Machine Learning Repository, and the clustering performance of the TSA is compared with the performances of the three well known clustering algorithms such as fuzzy c-means, k-means and k-medoids. The experimental results and comparisons show that the TSA based approach is highly competitive and robust optimizer for solving the partitional clustering problems.

Anahtar Kelimeler

• Clustering
• fuzzy c-means
• k-means
• k-medoid
• tunicate swarm algorithm

Kaynakça

1. A.K. Jain, Data clustering: 50 years beyond k-means, in: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, 2008, pp. 3-4.
2. A. Kaur, Y. Kumar, A new metaheuristic algorithm based on water wave optimization for data clustering, Evolutionary Intelligence, (2021) 1-25.
3. D. Karaboga, C. Ozturk, A novel clustering approach: Artificial Bee Colony (ABC) algorithm, Applied soft computing, 11 (2011) 652-657.
4. M. Karakoyun, O. İnan, İ. Akto, Grey Wolf Optimizer (GWO) Algorithm to Solve the Partitional Clustering Problem, International Journal of Intelligent Systems and Applications in Engineering, 7 (2019) 201-206.
5. V. Holý, O. Sokol, M. Černý, Clustering retail products based on customer behaviour, Applied Soft Computing, 60 (2017) 752-762.
6. L.M. Abualigah, A.T. Khader, M.A. Al-Betar, O.A. Alomari, Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering, Expert Systems with Applications, 84 (2017) 24-36.
7. Y. Marinakis, M. Marinaki, M. Doumpos, C. Zopounidis, Ant colony and particle swarm optimization for financial classification problems, Expert Systems with Applications, 36 (2009) 10604-10611.
8. S. Gong, W. Hu, H. Li, Y. Qu, Property Clustering in Linked Data: An Empirical Study and Its Application to Entity Browsing, International Journal on Semantic Web and Information Systems (IJSWIS), 14 (2018) 31-70.

9. A. Mekhmoukh, K. Mokrani, Improved Fuzzy C-Means based Particle Swarm Optimization (PSO) initialization and outlier rejection with level set methods for MR brain image segmentation, Computer methods and programs in biomedicine, 122 (2015) 266-281.
10. Á.A.M. Navarro, P.M. Ger, Comparison of clustering algorithms for learning analytics with educational datasets, IJIMAI, 5 (2018) 9-16.
11. I. Triguero, S. del Río, V. López, J. Bacardit, J.M. Benítez, F. Herrera, ROSEFW-RF: the winner algorithm for the ECBDL’14 big data competition: an extremely imbalanced big data bioinformatics problem, Knowledge-Based Systems, 87 (2015) 69-79.
12. L. Wang, X. Zhou, Y. Xing, M. Yang, C. Zhang, Clustering ECG heartbeat using improved semi-supervised affinity propagation, IET Software, 11 (2017) 207-213.
13. J. Zhu, C.-H. Lung, V. Srivastava, A hybrid clustering technique using quantitative and qualitative data for wireless sensor networks, Ad Hoc Networks, 25 (2015) 38-53.
14. R. Hyde, P. Angelov, A.R. MacKenzie, Fully online clustering of evolving data streams into arbitrarily shaped clusters, Information Sciences, 382 (2017) 96-114.
15. C.-H. Chou, S.-C. Hsieh, C.-J. Qiu, Hybrid genetic algorithm and fuzzy clustering for bankruptcy prediction, Applied Soft Computing, 56 (2017) 298-316.
16. J. Han, M. Kamber, J. Pei, Data mining concepts and techniques third edition, The Morgan Kaufmann Series in Data Management Systems, 5 (2011) 83-124.
17. S. Schaeffer, Graph clustering. Comput. Sci. Rev. 1 (1), 27–64, in, 2007.
18. B. Hufnagl, H. Lohninger, A graph-based clustering method with special focus on hyperspectral imaging, Analytica chimica acta, 1097 (2020) 37-48.
19. M.E. Celebi, H.A. Kingravi, P.A. Vela, A comparative study of efficient initialization methods for the k-means clustering algorithm, Expert systems with applications, 40 (2013) 200-210.
20. J.A. Hartigan, M.A. Wong, AK‐means clustering algorithm, Journal of the Royal Statistical Society: Series C (Applied Statistics), 28 (1979) 100-108.
21. P. Arora, S. Varshney, Analysis of k-means and k-medoids algorithm for big data, Procedia Computer Science, 78 (2016) 507-512.
22. M. Capó, A. Pérez, J.A. Lozano, An efficient approximation to the K-means clustering for massive data, Knowledge-Based Systems, 117 (2017) 56-69.
23. T. Velmurugan, Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data, Applied Soft Computing, 19 (2014) 134-146.
24. L. Kaufman, P.J. Rousseeuw, Partitioning around medoids (program pam), Finding groups in data: an introduction to cluster analysis, 344 (1990) 68-125.
25. J. Jędrzejowicz, P. Jędrzejowicz, Distance-based online classifiers, Expert Systems with Applications, 60 (2016) 249-257.
26. X. Qiu, Y. Qiu, G. Feng, P. Li, A sparse fuzzy c-means algorithm based on sparse clustering framework, Neurocomputing, 157 (2015) 290-295.
27. J.C. Dunn, A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, (1973).
28. J.C. Bezdek, Objective function clustering, in: Pattern recognition with fuzzy objective function algorithms, Springer, 1981, pp. 43-93.
29. A. Moreira, M.Y. Santos, S. Carneiro, Density-based clustering algorithms–DBSCAN and SNN, University of Minho-Portugal, (2005) 1-18.
30. S.J. Nanda, G. Panda, A survey on nature inspired metaheuristic algorithms for partitional clustering, Swarm and Evolutionary computation, 16 (2014) 1-18.
31. A. Nayyar, N.G. Nguyen, Introduction to swarm intelligence, Advances in swarm intelligence for optimizing problems in computer science, (2018) 53-78.
32. A. Nayyar, S. Garg, D. Gupta, A. Khanna, Evolutionary computation: theory and algorithms, Advances in swarm intelligence for optimizing problems in computer science, (2018) 1-26.
33. S. Saraswathi, M.I. Sheela, A comparative study of various clustering algorithms in data mining, International Journal of Computer Science and Mobile Computing, 11 (2014) 422-428.
34. C.S. Sung, H.W. Jin, A tabu-search-based heuristic for clustering, Pattern Recognition, 33 (2000) 849-858.
35. S.Z. Selim, K. Alsultan, A simulated annealing algorithm for the clustering problem, Pattern recognition, 24 (1991) 1003-1008.
36. M. Aslan, M. Gunduz, M.S. Kiran, JayaX: Jaya algorithm with xor operator for binary optimization, Applied Soft Computing, 82 (2019) 105576.
37. M.A. Rahman, M.Z. Islam, A hybrid clustering technique combining a novel genetic algorithm with K-Means, Knowledge-Based Systems, 71 (2014) 345-365.
38. Y. Marinakis, M. Marinaki, M. Doumpos, N. Matsatsinis, C. Zopounidis, A hybrid stochastic genetic–GRASP algorithm for clustering analysis, Operational Research, 8 (2008) 33-46.
39. Y. Kumar, P.K. Singh, A chaotic teaching learning based optimization algorithm for clustering problems, Applied Intelligence, 49 (2019) 1036-1062.
40. P. Shelokar, V.K. Jayaraman, B.D. Kulkarni, An ant colony approach for clustering, Analytica Chimica Acta, 509 (2004) 187-195.
41. G. Sahoo, A two-step artificial bee colony algorithm for clustering, Neural Computing and Applications, 28 (2017) 537-551.
42. X. Han, L. Quan, X. Xiong, M. Almeter, J. Xiang, Y. Lan, A novel data clustering algorithm based on modified gravitational search algorithm, Engineering Applications of Artificial Intelligence, 61 (2017) 1-7.
43. A. Khatami, S. Mirghasemi, A. Khosravi, C.P. Lim, S. Nahavandi, A new PSO-based approach to fire flame detection using K-Medoids clustering, Expert Systems with Applications, 68 (2017) 69-80.
44. A. Bouyer, A. Hatamlou, An efficient hybrid clustering method based on improved cuckoo optimization and modified particle swarm optimization algorithms, Applied Soft Computing, 67 (2018) 172-182.
45. S.I. Boushaki, N. Kamel, O. Bendjeghaba, A new quantum chaotic cuckoo search algorithm for data clustering, Expert Systems with Applications, 96 (2018) 358-372.
46. UCI Machine Learning Repository, https://archive.ics.uci.edu/ml/datasets.html, in, 2021.
47. S. Kaur, L.K. Awasthi, A. Sangal, G. Dhiman, Tunicate swarm algorithm: a new bio-inspired based metaheuristic paradigm for global optimization, Engineering Applications of Artificial Intelligence, 90 (2020) 103541.
48. S.N. Neyman, B. Sitohang, S. Sutisna, Reversible fragile watermarking based on difference expansion using manhattan distances for 2d vector map, Procedia Technology, 11 (2013) 614-620.
49. D.P. Mesquita, J.P. Gomes, A.H.S. Junior, J.S. Nobre, Euclidean distance estimation in incomplete datasets, Neurocomputing, 248 (2017) 11-18.
50. M. Luo, B. Liu, Robustness of interval-valued fuzzy inference triple I algorithms based on normalized Minkowski distance, Journal of Logical and Algebraic Methods in Programming, 86 (2017) 298-307.
51. H.-S. Park, C.-H. Jun, A simple and fast algorithm for K-medoids clustering, Expert systems with applications, 36 (2009) 3336-3341.
52. J. Berrill, The Tuniccafa, The Royal Society: London, (1950).